本文在算法技术上没有什么大的突破（主要是用BiLSTM），但是其Multi-task learning 运用在搜索引擎的entity recommendation和document ranku上以及把query的context（即之前的queriess）引入模型挺有意思。
Entity recommendation： 在搜集结果旁边出现的如，搜索“这个”的人也搜索“那个”；相关entity展示；
document rank：就是返回搜索结果。
模型简述：
（1）利用query有多个words进行BiLSTM学习出query
（2）利用context含有多个queries（每个都在（1）中学出了表达），进行aggregation operation形成一个context vector
（3）当前query vector + context vector 形成multi-task shared vector
（4）再在shared vector上加上一个FC（fully connected layer）对应到各个task
（5）各个task有自己的Loss function （Metric Learning： 点击过的documents或entities同当前的query更近）
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Improving Entity Recommendation with Search Log and Multi-Task Learning | |||||||||||
| 文献作者 | Huang, Jizhou and Zhang, Wei and Sun, Yaming and Wang, Haifeng and Liu, Ting | ||||||||||
| 文献发表年限 | 2018 | ||||||||||
| 文献关键字 | |||||||||||
| Entity recommendation; Baidu； BiLSTM | |||||||||||
| 摘要描述 | |||||||||||
| Entity recommendation, providing search users with an improved experience by assisting themin finding related entities for a given query, has become an indispensable feature of today’s Websearch engine. Existing studies typically only consider the query issued at the current time step while ignoring the in-session preceding queries. Thus, they typically fail to handle the ambiguous queries such as “apple” because the model could not understand which apple (company or fruit) is talked about. In this work, we believe that the in-session contexts convey valuable evidences that could facilitate the semantic modeling of queries, and take that into consideration for entity recommendation. Furthermore, in order to better model the semantics of queries, we learn the model in a multi-task learning setting where the query representation is shared across entity recommendation and context-aware ranking. We evaluate our approach using large-scale, real-world search logs of a widely used commercial Web search engine. The experimental results show that incorporating context information significantly improves entity recommendation, and learning the model in a multi-task learning setting could bring further improvements. | |||||||||||
